Anomaly detection has been applied to computer security, network security, and identifying defects in semiconductors, superconductor conductivity, medical applications, testing computer programs, inspecting manufactured devices, and a variety of other applications. The principles that are typically used in anomaly detection include identifying normal behavior and a threshold selection procedure for identifying anomalous behavior. Usually, the challenge is to develop a model that permits discrimination of the abnormalities.
In computer security applications one of the critical problems is distinguishing between normal circumstance and “anomalous” or “abnormal” circumstances. For example, computer viruses can be viewed as abnormal modifications to normal programs. Similarly, network intrusion detection is an attempt to discern anomalous patterns in network traffic. The detection of anomalous activities is a relatively complex learning problem in which the detection of anomalous activities is hampered by not having appropriate data and/or because of the variety of different activities that need to be monitored. Additionally, defenses based on fixed assumptions are vulnerable to activities designed specifically to subvert these assumptions.
To develop a solution for an anomaly detection problem, a strong model of normal behaviors needs to be developed. Anomalies can then be detected by identifying behaviors that deviate from the model.